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The [] represent spatial averaging and the overbar represent temporal averaging, and theprime and * represent temporal and spatial perturbations respectively. Such that[Eq. 3]When the mean wind speed is used (i.e. just the first term in Eq. 2] then the calculatedannual mean wind power density for the large test area is given by Figure 7. This is aserious underestimate as time variance contribution and spatial variance contributions aremissing.When the mean of the cube of the 6-hourly wind speed is used then the calculated annualmean wind power density is given by Figure 8. This estimate included the time variancepart at the coarse resolution that was missing in Figure 7. However the contribution towind power density from the spatial variance of wind speed within each 50 x 50 kmblock is still missing.Figure 9, shows the calculated wind power density using the mean of the 6-hourly windspeed, plus the contributions temporal and spatial variance of wind speed. The temporalvariance comes from a Weibull distribution fitted to the 6-hourly time series of winds;given by a scale factor and a shape factor. The temporal variance can be expressed interm of the shape factor. The spatial variance comes from sum of orography androughness variance contributions, as described here. This estimate shows somewhatincreased wind resources in areas of heterogeneous elevation and roughness lengthcompared to the estimate based on the mean of the cube of wind speed (Figure 8).Another feature of the method making use of the spatial variance of wind speed over the50 x 50 km blocks, is that the distribution of wind power density can be estimated, basedon an assumed distribution of wind speed distribution. In Badger et al (2010) a Gaussiandistribution of wind speed was assumed and found to suitable for most on-land cases,though less appropriate for coastal cases. Figure 10 shows the mean wind power densityfor the windiest 10-percentile of each 50 x 50 km block. Using this map we can see thatthe mountainous areas have high wind power density areas, compared to the blocks’mean power density. Whereas in the plain and offshore areas, the wind power density ofthe windiest sites is not much greater than the blocks’ mean wind power density.Such knowledge of the distribution of the wind power density is of great value, as itgives information about wind resources in a manner suitable for the exploitation of wind.Wind turbines are not randomly distributed. They are sited at the most favourable sites interms of wind resources and after consideration of relevant constraints. Unlike themethod described here, direct application of coarse resolution global wind datasets doesnot provide the distribution of the wind resource at a spatial scale smaller than the dataresolution.5 ApplicationThe datasets comprising the Global Wind Atlas will be created to suit the needs of thepolicy makers and energy planners. Through dialogue with the Integrated AssessmentModel (IAM) community, the required specifications of the Global Wind Atlas datasetswill be determined.The datasets will give both spatial and temporal variation of wind resource. Temporalvariation of wind resource can be of particular importance when consideration of energyRisø International Energy Conference 2011 Proceedings Page 223